Meta-LAD: Developing a Learning Analytics Dashboard with a Theoretically Grounded and Context-Specific Approach

The use of Learning Analytics Dashboards (LADs) has gained popularity as a means of supporting the self-regulated learning (SRL) skills of learners in large-scale online courses. Despite many studies proposing LAD designs, LADs are often criticized for their weak theoretical foundations, lack of actionable feedback, and tendency to encourage excessive social comparison. Furthermore, many LAD designs have missed context-specific details. Hence, it is not uncommon for some dashboard designs to have negative effects on learners, such as discouragement or anxiety. In this study, we designed the Meta-LAD, a LAD that supports SRL processes using theoretical and contextual foundations. We used data from a credit-bearing Massive Open Online Course (MOOC) on supply chain management to contextually ground the dashboard. We performed usability testing interviews to evaluate the design and confirmed that the Meta-LAD could fulfill learners' needs for references and actionable feedback. This study contributes to the field of online learning by presenting a theoretically grounded and contextually specific LAD design process. This paper expands the understanding of how to support SRL in MOOCs.